MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 449: 65–82, 2012doi: 10.3354/meps09543

Published March 8

INTRODUCTION

Chesapeake Bay is the largest estuary in theUnited States, and the Susquehanna River at thehead of the bay is responsible for >50% of freshwaterinput (Kemp et al. 2005). There is a distinct seasonalpattern in river flow, with high discharge from latewinter to spring and low to moderate discharge fromsummer to fall. The flow largely controls salinity gra-dients and therefore stratification, pycnocline depth,and the location of the turbid zone (Schubel &Pritchard 1986). The highest concentration of sus-pended particles is usually found at the limit of saltintrusion in the vicinity of the sharp salinity gradientwhere freshwater and seawater converge. This

region, called the estuarine turbidity maximum(ETM), occurs in a channel that is maintained bydredging. Gravitational circulation induced by thecomposite influence of tidal exchange and fresh -water discharge generates 2-layer circulation andentraps particles in the ETM. The trapping is primar-ily due to the convergence and recirculation at thefreshwater/saltwater interface, but it is also influ-enced by a variety of factors such as stratification,resuspension, flocculation, tides, settling velocity,sedimentation rate, topography, and wind (Sanfordet al. 2001).

The oligohaline area of the bay, where the ETM islocated, encompasses 11% of the total bay area andreceives organic matter from both terrestrial and

© Inter-Research 2012 · www.int-res.com*Email: dlee@umces.edu

Community metabolism and energy transfer in theChesapeake Bay estuarine turbidity maximum

Dong Y. Lee1,*, David P. Keller2, Byron C. Crump1, Raleigh R. Hood1

1Horn Point Laboratory, University of Maryland Center for Environmental Science, 2020 Horns Point Road, Cambridge, Maryland 21613, USA

2IFM-GEOMAR, Leibniz-Institut für Meereswissenschaften, Düsternbrooker Weg 20, 24105 Kiel, Germany

ABSTRACT: In an effort to identify the key mechanisms controlling biological productivity andfood web structure in the Chesapeake Bay estuarine turbidity maximum (ETM), we measuredplankton community metabolism on a series of surveys in the upper Chesapeake Bay during thewinter and spring of 2007 and 2008. Measured quantities included primary production, bacterialproduction, planktonic community respiration, and algal pigment concentrations. These measure-ments revealed a classic minimum in photosynthesis in the vicinity of the ETM. Temporal variabil-ity in plankton community metabolism, primary production, respiration, and bacterial productionwere highest in the southern oligohaline region down-estuary of the ETM and appeared to be dri-ven by dynamic bio-physical interactions. Elevated primary production and community respira-tion in this region were often associated with the presence of mixotrophic dinoflagellates. Thedinoflagellate contribution to primary production and respiration appeared to be particularlylarge as a result of their mixotrophic capabilities, which allow them to obtain energy bothautotrophically and heterotrophically. The present study suggests that mixotrophic dinoflagellatesplay a key role in the pelagic food web in the oligohaline region of Chesapeake Bay, supplyingmost of the labile organic matter during late winter and spring and also providing a vector fortransferring microbial production to mesozooplankton.

KEY WORDS: Estuarine turbidity maximum · Plankton community metabolism · Mixotrophicdinoflagellate · Estuarine food web · Chesapeake Bay

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Mar Ecol Prog Ser 449: 65–82, 201266

aquatic sources. Estimating the quantity and qualityof this organic matter is difficult due to the diverseorigins and complex biogeochemical reactions thatoccur between dissolved and particulate organicmatter and living organisms (Simon et al. 2002). Inlakes, stable isotope analysis of organic carbon indi-cates that a greater fraction of heterotrophic metabo-lism is fueled by terrestrial organic matter than byautochthonous primary producers when environ-mental conditions limit autotrophic production (Car-penter et al. 2005, Pace et al. 2007). In ChesapeakeBay, primary production and chlorophyll a (chl a)concentrations are lowest in the oligohaline area(Smith & Kemp 1995), presumably due to light limita-tion (Fisher et al. 1999), suggesting that, as in lakes,terrestrial organic matter plays an important role inheterotrophic production.

Understanding the relative importance of auto ch -thonous algal production compared to allochthonousinput in the ETM is particularly important becausethis region is an area of high larval recruitment formany fish species including striped bass Morone sax-atilis and white perch M. americana (North & Houde2006). Mesozooplankton such as the calanoid cope-pods Eurytemora affinis and Acartia tonsa and thefreshwater cladoceran Bosmina longirostris are alsovery abundant (Roman et al. 2001). Elevated meso-zooplankton biomass during periods of high freshwa-ter flow enhances larval fish production (Kimmel &Roman 2004), but the sources and pathways oforganic matter fueling mesozooplankton productionare unknown.

One potential source of organic matter for meso-zooplankton in the ETM is suspended detritus. As apart of a dynamic food web, the microbial loop the-ory emphasizes the importance of detrital produc-tion pathways (Pomeroy 1974, Azam et al. 1983).Expanding on this theory, Baross et al. (1994) pro-posed the ‘microbial shunt’, which theorizes that, ifbacteria and detritus are directly consumed bycopepods, the shortened production pathway wouldresult in higher energy conservation and transferefficiency than the microbial loop. The shunt theoryis in part supported by the effective grazing capa-bility of many copepods on a broad size spectrum ofprey items (Heinle et al. 1977, Boak & Goulder1983). However, this unique pathway would notresult in high copepod production if the quantityand quality of detrital organic matter limits thegrowth of copepods.

Direct measurements of oxygen changes providea tool for estimating the relative contributions ofautotrophic and heterotrophic processes in aquatic

systems and therefore quantifying the relativeimportance of autochthonous versus allochthonouscarbon sources in the food web. Net ecosystemmetabolism (NEM), estimated by subtracting com-munity respiration (Rcomm) from gross primary pro-duction (GPP), can be used to assess the aggregateresponse of a wide variety of autotrophic and het-erotrophic species in a community to environmentaland anthropogenic influences which can reveal thetrophic status of an ecosystem (Caffrey 2004). Estu-arine eutrophication due to anthropogenic distur-bances often causes negative NEM (also called netheterotrophy), implying that more organic matter isconsumed by respiration than produced by auto -trophic growth (Hopkinson & Vallino 1995). In theoligohaline area of the bay, both GPP and Rcomm arelow, but the latter exhibits high spatial and temporalvariations because of differences in uptake ratesand growth efficiency on terrestrial and autotrophicorganic matter by hetero trophs (Smith & Kemp1995, 2003). In general, NEM in the oligohaline isnet heterotrophic, though often with significant sea-sonal variations (Kemp et al. 1997) due to the highorganic loads. In contrast, in areas where GPP ishigh due to abundant light and inorganic nutrientsthe ecosystem can have positive NEM (also callednet autotrophy), implying that more organic matteris produced by local autotrophic growth than con-sumed by heterotrophic respiration.

Here we describe results from a series of temporaland spatial surveys in the Chesapeake Bay ETMregion. These measurements provide insight into themetabolic demands of different plankton communi-ties in relation to salinity and turbidity. We speculatethat the balance between autotrophic and heterotro-phic processes can vary significantly with time and inspace, especially during late winter and spring, dueto temporal variability in river flow. Although theETM appears to play an important role in secondaryproduction, the major sources of the organic matterthat fuel higher trophic level production (e.g. fresh-water, marine, local) are currently unknown. Theobjectives of the present study are to use direct mea-surements of primary production and respiration incombination with bacterial production (BP), waterquality, and algal pigment data to (1) characterize thespatial and temporal variability in GPP and respira-tion, (2) determine which members of the phyto-plankton community contribute to GPP that mightfuel growth of higher-trophic-level organisms, and(3) compare GPP and BP to identify food web path-ways through which production is transferred tohigher trophic levels.

Lee et al.: Energy transfer in Chesapeake Bay

MATERIALS AND METHODS

Study site

The present study was conducted on a 76 km axialtransect along the main channel of the upper Chesa-peake Bay from Turkey Point lighthouse (latitude:39° 26.9’ N; 0 km) near the mouth of SusquehannaRiver to the Chesapeake Bay Bridge (latitude:38° 59.8’ N; 76 km), Maryland, USA (Fig. 1). River dis-charge data provided by United States GeologicalSurvey were recorded at the Conowingo Dam locatedin the lower Susquehanna River (39° 39’ 28.1’’ N,76° 10’ 28.2’’ W; http:// waterdata. usgs. gov/ md/nwis)according to the methods of Buchanan & Somers(1969). Hereafter, the oligo haline area is often seg-mented into 3 regions: (1) the northern region locatedfrom 0 to 30 km from the mouth of Susquehanna Riverto the landward side of ETM, (2) the ETM region lo-cated from 30 to 45 km, and (3) the southern region lo-cated from 45 to 80 km. A total of 6 cruises were con-

ducted, (22 to 26 February 2007, 23 to 26 January2008), 2 in early spring (9 to 15 April 2007, 17 to 23April 2008), and 2 in late spring (8 to 14 May 2007, 16to 22 May 2008) on the RV ‘Hugh R. Sharp’. Thespring cruises were designed to maximize observa-tions during the time periods of high river dischargeand organic matter loading. Two quasi-synoptic axialsurveys were performed at the beginning and end ofeach cruise. These consisted of 11 CTD (Sea-BirdElectronics) casts equipped with fluorescence, oxy-gen, and optical backscatter sensors. All axial surveyswere done in <8 h from 06:00 to 14:00 h. Salinity andturbidity were mapped as contour plots, and it shouldbe noted that the actual scale of the x-axis (river distance) to y-axis (depth) on the contour plots is dis-torted by about 300:1.

Field sampling procedures

Five stations out of the 11 CTD sites were selectedalong the axial surveys for water sample analyses. Ateach of these 5 stations, which included 2 stations inthe north, 1 in the ETM, and 2 in the south, sampleswere collected for measuring plankton communitymetabolism, water quality, pigment analysis, and BPsimultaneously with 20 l Niskin bottles mounted on aCTD frame at 0.5 m below the surface, in the pycno-cline, and 0.5 m above bottom. At the freshwaterend-member (furthest up-estuary) station wherethere was no pycnocline, mid-water samples werecollected at mid-depth. Under low light conditionswe gently transferred water from each depth into 24 lbuckets equipped with a stir bar to ensure the homo-geneity of water samples. At least 10 min after thetransfer, samples for measuring oxygen productionand consumption were siphoned through Tygon lab-oratory tubing into triplicate 60 ml borosilicate bio-chemical oxygen demand (BOD) bottles, allowingeach bottle to overflow 3 times its volume. Samplesfor initial oxygen concentration were fixed immedi-ately. Oxygen concentration was measured using theWinkler titration method (Carpenter 1965), with anautomated photometric endpoint detection systemhaving a minimum precision of 0.01% (SensorenInstrument System). The light−dark bottle oxygenmethod (Kemp et al. 1992) was used for making pri-mary production and respiration measurements withsamples incubated on rotating transparent andopaque flow-through incubators (12 rotations min−1)respectively, at ambient water temperature (±1°C) onshipboard, to prevent any biased rates due to settlingof suspended material.

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Fig. 1. The oligohaline area of Chesapeake Bay, which isapproximately 80 km along a shipping channel andstretches from Havre de Grace (latitude: 39°28.3’N) at themouth of the Susquehanna River to the Chesapeake BayBridge (latitude: 38° 59.8’ N), Maryland, USA. A CTD pro-filer was cast at 11 stations from the south to the north within8 h from 06:00 to 14:00 h. Water samples were collected from5 stations among the 11 stations. Sampling station positions

are approximate

Mar Ecol Prog Ser 449: 65–82, 2012

Primary production measurements

Surface water samples were obtained from approxi-mately 0.5 m below the surface between sunrise andmidday (~07:00 to 13:00 h). Then, BOD bottles werefilled and covered with neutral-density screens allow-ing passage of 5 different light levels of surface irradi-ance, and they were incubated in a rotating deck incubator under sunlight for 24 h. Underwater irradi-ance levels were measured using a PRR-600 PAR sen-sor (400 to 700 nm; Biospherical Instruments) or SecchiDisc depth following CTD casts. The Secchi Disc wasused only in late spring 2008 when the radiometermalfunctioned. The Lambert-Beer Law was used tocalculate diffuse attenuation coefficients and euphoticdepths (1% of surface irradiance level), except in latespring 2008 when diffuse attenuation coefficientswere derived by dividing 1.7 by Secchi Disc depth.Theoretical depths of the 5 simulated in situ light levels were also calculated using the Lambert-Beerlaw for vertical integration (Parsons et al. 1984). Thenet primary production rate, measured at each lightlevel, was integrated vertically over the euphoticdepth using a trapezoidal method. One potential prob-lem with the integration method is the underestimationof primary production because the 5 light levels weredistributed under <50% of surface irradiance. How-ever, the theoretical depths of the 5 light levels wereuniformly distributed over the euphotic zone becausesurface irradiance decreased most rapidly at high lightlevels ranging from 100 to 40% of surface irradiance.This was confirmed by the underwater light measure-ments in all seasons. GPP at each station was deter-mined by adding Rcomm in the euphotic zone to net pri-mary production assuming constant daily respiration(Hopkinson et al. 1989). The validity of this assumptionwas confirmed by carrying out 8 experiments at thefreshwater end-member, ETM surface, ETM bottom,and seawater end-member at different temperaturesranging from 7 to 23°C. A set of three 60 ml BOD bot-tles were sampled every 6 h during 24 h incubation,and the regression slopes for ETM stations resulted inan average of r2 = 0.92 (p < 0.001), while r2 = 0.97 (p <0.001) for all other areas.

Respiration measurements

Rcomm and respiration of plankton filtered through a3 µm polycarbonate membrane filter (R<3µm) weremeasured on surface, middle, and bottom water sam-ples as decreases in oxygen concentration in darkBOD bottles for a period of either 12 h in late winter

or 6 h in spring. The filtration for R<3µm was doneusing a reverse gravity method (Crump et al. 1998).Volumetric respiration rates were calculated as thedifference in the means between initial and final oxy-gen concentrations. Respiration rates at the 3 depthswere multiplied by the depth of each region, andthen summed over the water column to get the totalwater column rate.

Size-fractionated respiration measurements

We performed size-fractionated respiration ratemeasurements to determine the quantitative signifi-cance of different size classes in Rcomm along withsize-fractionated chl a measurements. Standard for-ward filtration was used in 2007 by gently pouringwater through a sequence of filters. Water sampleswere screened through 20, 10, and 3 µm Nitex meshand collected in stirrer-equipped buckets. The <3 µmfiltration may include the oxygen demand of bacte-ria, cyanobacteria, and other small heterotrophs andautotrophs, which are mostly composed of prokary-otes. The <10 and >3 µm filtration may include therespiration of heterotrophic flagellates, ciliates, parti-cle-attached bacteria, phytoplankton, and micropro-tozoa (Hopkinson et al. 1989). The <20 µm filtrationwould exclude the respiration by macrozooplankton,mesozooplankton, large ciliates, protozoa, and largephytoplankton. Incubation methods were the sameas for the normal respiration rate measurements. Thesize-fractionated measurements can be biased by fil-tration artifacts (e.g. breaking particles, disturbingorganisms, or decreasing predation), resulting, forexample, in increased total respiration in filteredsamples compared to whole water. This problemsometimes occurred when water was filtered througha 63 µm screen, but it did not happen with thesmaller filter sizes.

Bacterial production measurements

Bacterial production in whole water and in waterfiltered through a 3 µm polycarbonate membrane fil-ter (BP<3µm) was measured on surface, middle, andbottom water samples using the incorporation rate of3H-leucine into macromolecules during 1 h incuba-tions (Kirchman 1993). BP>3µm, which is referred tohere as the production of bacteria in >3 µm watersamples, was calculated by subtracting BP<3µm fromBP. The 3H-leucine incorporation rate was convertedinto the production rate assuming a ratio of cellular

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Lee et al.: Energy transfer in Chesapeake Bay

carbon to protein of 0.86, a fraction of leucine in pro-tein of 0.073, and an intracellular leucine isotopedilution of 2 (Crump et al. 2007). To compare the rel-ative magnitude of organic matter production bybacteria and autotrophs, a photosynthetic quotient of1.2 (Smith & Kemp 1995) was assumed to convert tocomparable carbon units. Note that GPP was inte-grated over the euphotic depth, whereas BP wasintegrated over the water column.

Total bacterial respiration (BR) could not be mea-sured directly because it is not possible to physicallyseparate eukaryotes from particle-attached bacteria.Therefore, the portion of BR accomplished by >3 µmbacteria (BR>3µm) was estimated using BP>3µm and thelinear regression relationship between BP<3µm andR<3µm. This approach assumes that <3 µm bacteriashare the same conversion factor with >3 µm bacteria(Iriberri et al. 1990). BR was then calculated byadding measurements of R<3µm and estimates ofBR>3µm. Percentages of total BP and BR, derived fromthe calculation above, were used to calculate bacter-ial growth efficiency (BGE).

Phytoplankton pigment analyses

Pigment samples for high-performance liquid chro-matography (HPLC) and fluorometric analysis werecollected in duplicate at surface, middle, and bottomdepths by filtering water under low light conditionsthrough 25 mm GF/F filters. The filters were stored at−80°C for HPLC and −20°C for fluorometric analysisusing a low temperature freezer. Upon return toshore, HPLC pigment analysis was performed byHorn Point Laboratory analytical services usingmethods described in Van Heukelem & Thomas(2001). In addition, chl a and pheophytin a were mea-sured by extracting the pigments in 90% acetone andthen determined using a fluorometer (Arar & Collins1997). The following pigments (and their associatedphytoplankton groups or degradation sources) wereused in the statistical analyses in the present study:chl a, peridinin (dinoflagellates), fucoxanthin (dia -toms), alloxanthin (cryptophytes), zeaxanthin (cyano -bacteria), and degraded pigments chlorophyllide a,pheophorbide a, pheophytin a, and total pheophytin(the sum of degraded pigments).

Statistical analyses

Due to significant correlations between salinity andphytoplankton pigments, which is common in estuar-

ies (Brand 1984), partial correlation analysis was usedkeeping the salinity effect constant using SAS (SASInstitute). This allows interpretation of correlationsamong biological variables without the potentiallyconfounding influence of common correlations withsalinity. Data point outliers were excluded based onCook’s D-test statistic (Chatterjee & Hadi 2006). Thepoints excluded from data reported here include 1Rcomm and 2 R<3µm rates measured in early spring 2007that were an order of magnitude higher than themean respiration rate on the same axial survey. Testsfor equal variance and normality were checked withAnderson-Darling and Bartlett’s tests, respectively,with a significance level of 0.01. Due to seasonal dif-ferences in environmental conditions, regression sta-tistics for phytoplankton pigments with communitymetabolism were performed by season.

RESULTS

The average river discharge in 2007 (1198 m3 s−1)was lower than in 2008 (1871 m3 s−1) during late -winter cruises (Fig. 2). For 2 wk prior to the cruise in2007, the mean of water temperature was 0.1°C andit resulted in the formation of surface ice covering 3quarters of upper Chesapeake Bay. As a result,weaker salinity gradients and more well-mixedwater columns were observed in 2007, and the inter-face where the 1 psu isohaline meets the bottom waslocated further north near 0 km in 2007 (Fig. 3).Although the turbidity was the lowest among thestudied seasons in late winter, the ETMs wereobserved between 15 and 25 km in the bottom water.Prior to the early-spring cruises, daily river dis-charges reached >6000 m3 s−1 in 2007 and >8000 m3

s−1 in 2008; this resulted in strong water-column strat-ification and a more pronounced ETM, leading to thehighest turbidity between 25 and 40 km. Althoughriver discharge was higher in 2008 than in 2007,

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Fig. 2. Daily Susquehanna River discharge at ConowingoDam in 2007 and 2008 was obtained from the United StatesGeological Survey (USGS 01578310 station). Arrows indi-

cate the timing of cruises

Mar Ecol Prog Ser 449: 65–82, 2012

water-column stratification and turbidity werestronger in 2007. The difference may have beencaused by tidal effects, because the timing of theaxial survey was from falling ebb to full flood in 2007,whereas the timing was from falling flood to full ebbflows in 2008. Also, in 2007 sharp salinity gradientsand strong horizontal pycnoclines were observedfrom 20 to 80 km and those were located between 5and 10 m depth. In both years the 1 psu isohaline waslocated down-bay near 20 km, which was furthersouth than in the late winter. In contrast, the meanriver discharge dropped to <1400 m3 s−1 during thelate-spring cruises, resulting in a weaker salinity gra-dient. This likely also caused the turbid area to man-ifest in a broad region from 30 to 50 km. Euphoticdepths ranged from 1.7 to 3.8 m in late winter, 1.6 to3.8 m in early spring, and 1.5 to 4.4 m in late spring,and they generally deepened towards the south.Also, on average, <15% of the water column wasrepresented by the euphotic zone in all seasons.

Phytoplankton community composition

Previous studies have revealed that there are di-verse phytoplankton communities that co-exist in theupper Chesapeake Bay (Adolf et al. 2006), presum-ably due to the convergence of salt and fresh water.We observed dinoflagellates, diatoms, and crypto-phytes varying in time and space. In general, thephytoplankton communities in the study area weredominated by dinoflagellates and diatoms in terms oftheir biomass, with substantial spatial and temporalvariations. It appears that dinoflagellates and diatoms

contributed to the increases in chl afrom 45 to 80 km, whereas freshwaterdiatoms were mainly responsible forthe increases in chl a from 0 to 30 kmin early spring (Fig. 4). The dino -flagellate community in the high chl aregions toward the south was domi-nated by Heterocapsa rotundatum(also known as Katodinium ro tun -datum) and Prorocentrum minimum.These are mixo trophic (auto trophicand phago trophic) organisms thatare all capable of grazing on bacteriaand cryptophytes of various sizesand shapes (Jeong et al. 2005). Weobserved subsurface maximum con-centrations of mixotrophic dino flag -ellates throughout the pycnoclinefrom 40 to 80 km.

The diatom community was mainly composed ofsmall centric species (<10 µm), but the biomass ofdiatoms was more patchy and less abundant thandinoflagellates. Comparably lower concentrations

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Fig. 3. Contour plots of salinity (dashed lines; unit: psu) and turbidity (shading;in nephelometer turbidity units) measured during 11 CTD casts in (a,b) latewinter, (c,d) early spring, and (e,f) late spring in 2007 and 2008. The x-axis ofeach plot presents distances from Turkey Point lighthouse (0 km) to the

Chesapeake Bay Bridge (80 km) along a shipping channel

Fig. 4. The 2 yr means of depth-averaged chlorophyll a,dino flagellate (peridinin), and diatom (fucoxanthin) concen-trations in (a) late winter, (b) early spring, and (c) late spring

for 2007 and 2008. Error bars: 1 SEM (n = 4 per season)

Lee et al.: Energy transfer in Chesapeake Bay

of fucoxanthin were observed with less distinctspatial trends (Fig. 4). We also observed low con-centrations of cryptophytes and cyanobacteriathroughout the upper bay, with the concentrationsconsistently <1.2 µg l−1 for cryptophytes and0.1 µg l−1 for cyano bacteria.

Plankton community metabolism increased dra-matically to the south and varied seasonally (Fig. 5).The lowest and highest GPP values were found inearly spring and late spring, respectively, and GPPvalues were similar in late winter and early spring. Incontrast, Rcomm was higher in early spring than in latewinter, and this increase in Rcomm appeared to be dueto an increase in R<3µm (Fig. 5c). The mean GPP perchl a (i.e. the assimilation number) was not differentbetween stations during the entire study (p > 0.05).

The average NEM at the surface indicated netautotrophy in late winter and late spring, but net

hetero trophy in the ETM region in early spring(Fig. 6). In contrast, calculations of NEM throughoutthe water column indicated net heterotrophy, reveal-ing large contributions to respiration from hetero -trophs in deeper water below the euphotic zone.Combined production by phytoplankton and bacteriawas always greater south of the ETM station due toelevated rates of GPP (Fig. 7). In early spring, themean contribution by the 2 communities was rela-tively balanced when averaged across the wholetransect, but the contribution from BP was muchhigher (76%) than GPP in the ETM. In late spring thecontribution of autotrophic carbon was >65% at allstations along the transect.

Statistical analysis for gross primary production

Although correlation coefficients and statistical sig -nificance vary by seasons, the results derived usingthe entire data set captured general patterns andmagnitudes of correlations. The analysis revealedthat GPP was significantly correlated with several en-vironmental and biological factors (Table 1). How-ever, there were also significant correlations betweensalinity and most of the phytoplankton pigments (except cyanobacteria), making it difficult to interpretthese results due to multicollinearity.

Partial correlation analysis, keeping salinity con-stant, was therefore employed and resulted in an

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Fig. 5. The 2 yr means of (a) gross primary production (GPP),(b) community respiration (Rcomm), and (c) respiration filteredthrough 3 µm Nitex mesh (R<3µm) along the mainstem ofChesapeake Bay in different seasons for 2007 and 2008. Inorder to account for the 5-fold difference in the water col-umn depth between the seawater end-member and fresh-water end-member, vertically integrated respiration rates(b,c) were divided by water column depths. Error bars:

1 SEM (n = 4 per season)

Fig. 6. The 2 yr means of net ecosystem metabolism (NEM)rates (a) near the surface and (b) in the total water columnalong the mainstem of Chesapeake Bay in different seasonsfor 2007 and 2008. Error bars: 1 SEM (n = 4 per season)

Mar Ecol Prog Ser 449: 65–82, 2012

overall decline in the correlation coefficients for allvariables. However, significant relationships werestill found with GPP, except with the cryptophyte -indicating pigment alloxanthin. Among the pigments,chl a, peridinin, fucoxanthin, and zeaxanthin were allsignificantly correlated with GPP, indicating thatchanges in total phytoplankton abundance and,specifically, the abundance of individual phytoplank-ton groups were driving changes in GPP (Table 1).However, the contribution of cyanobacteria to GPPwas much lower than dinoflagellates and dia toms be-cause of low concentration. Phytoplankton pigmentswere also strongly correlated with salinity, suggestingthat salinity control/stress can be an important factorin determining the phytoplankton community com-position. Among the environmental variables, tem-perature was the most important factor explainingthe variability in GPP, suggesting that there was sea-sonal thermal kinetic control of phytoplankton pro-duction. Total suspended sediment (TSS) was nega-tively correlated with GPP and eu photic depth waspositively correlated with GPP because high TSS con-centrations result in lower light availability, whichlowers GPP (Xu et al. 2005). It should be noted, how-ever, that TSS and euphotic depth were not cor -related with GPP on any of the individual cruises(data not shown), suggesting that seasonal changesin TSS and euphotic depth (rather than spatialchanges) were driving these correlations.

Season by season regression analyses revealed thatthe chl a concentration explained ≥72% (p < 0.01) ofthe variation of GPP in late winter and late spring(Fig. 8), but only 40% (p < 0.01) of the variation inearly spring (graph not shown). Significant fractionsof GPP were also explained by dinoflagellate concen-trations (Fig. 8). The dinoflagellate pigment explained≥70% (p < 0.01) of GPP in late winter and late spring,

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Fig. 7. Percentages of 2 yr carbon production rates producedby phytoplankton (gross primary production, GPP), bacteria>3 µm (BP>3µm), and bacteria <3 µm (BP<3µm) in (a) late win-ter, (b) early spring, and (c) late spring (left y-axis). Lines

are the sums of the 3 production rates (right y-axis)

Salinity Temp. TSS Euphotic Chl a Dino- Diatom Crypto- Cyano- depth flagellate phyte bacteria

GPP 0.46** 0.58** −0.31* 0.32* 0.74** 0.64** 0.33* nr 0.58**Temperature −0.24**0 *−0.34** 0.32* 0.25 nr nr 0.21 0.35**TSS −0.21**0 −0.61** nr nr −0.33* nr nrEuphotic depth 0.60** nr nr 0.26* 0.23 nrChl a 0.65** 0.83** 0.39** nr 0.43**Dinoflagellate 0.57** nr nr nrDiatom 0.34** nr 0.55**Cryptophyte 0.54** nrCyanobacteria nr

Table 1. Partial correlation matrix of salinity and environmental and biological variables at the surface with gross primary pro-duction (GPP). The correlation excludes salinity influence if any. TSS: total suspended sediment; chl a: chlorophyll a; nr: no

relationship if r < 0.2; **p < 0.01; *0.01 < p < 0.05; n = 60

Lee et al.: Energy transfer in Chesapeake Bay

but only 3% (p = 0.45) in early spring (graph notshown). Whereas the diatom pigment explained a rel-atively smaller fraction of the GPP variability: 2% (p =0.59) in late winter, 27% (p < 0.05) in early spring,and 40% (p < 0.01) in late spring. The stronger corre-lation between GPP and diatom pigment in earlyspring compared to dinoflagellate pigment was, inpart, caused by high diatom concentrations between0 and 35 km, which corresponded to an increase inGPP in the area (Figs. 4 & 5).

Statistical analyses for community respiration

Here also, partial correlation was used due to mul-ticollinearity. Chl a, peridinin, pheophytin a, andBP were all positively correlated with Rcomm, whilechl a and peridinin were not correlated with R<3µm

(Table 2). Positive correlations between TSS and de -graded pigments, including pheophorbide a, pheo -phytin a, and total pheophytin, suggest that the ETMwas a site for deposition of degraded pigments.Among all variables, temperature is considered to beone of the most important influencing the respiration

of bacteria (Shiah & Ducklow 1994), plankton (Sam-pou & Kemp 1994), and the entire estuarine commu-nity (Caffrey 2004). It is therefore not surprising thata significant correlation was found between temper-ature and both Rcomm and R<3µm. However, tempera-ture was not significantly correlated with respirationon any of the individual cruises (data not shown),suggesting that seasonal changes in temperature(rather than spatial changes) were driving these cor-relations. More interestingly, TSS was inversely cor-related with Rcomm in early spring (r = −0.34, p < 0.01),late spring (r = −0.50, p < 0.01), and for the entire dataset (Table 2).

Variations in chl a explained ≥62% (p < 0.01) of thevariations in Rcomm in late winter and late spring(Fig. 9), but only 32% (p < 0.01) in early spring (graphnot shown). Also, ≥62% (p < 0.01) of the variability inRcomm was explained by the dinoflagellate pigment inlate winter and late spring, but only 5% (p = 0.08) inearly spring (graph not shown). These results suggestthat variations in the dinoflagellate populationsplayed a very large role in driving Rcomm during latewinter and late spring. The significant correlation

73

Fig. 8. Regression analysis of gross primary production withthe concentrations of (a,b) surface chlorophyll a and (c,d) thedinoflagellate-indicating pigment peridinin in late winterand late spring of 2007 and 2008. The best-fit line is calcu-lated from a least-squares method, and the 2 dotted lines are95% prediction bounds; n = 20. None of the y-intercepts

were different from 0 (p > 0.05)

Fig. 9. Regression analysis of community respiration and theconcentrations of (a,b) chlorophyll a and (c,d) the dinoflagel-late-indicating pigment peridinin in late winter and latespring of 2007 and 2008. The best-fit line is calculated froma least-squares method, and the 2 dotted lines are 95% pre-diction bounds; n = 60. The y-intercepts were significantly

different from 0 (p < 0.01) in late spring (b,d)

Mar Ecol Prog Ser 449: 65–82, 2012

between dinoflagellates and cryptophy tes also re -veals a spatial co-occurrence between the 2 commu-nities which may be indicative of a prey−predatorrelationship.

Size-fractionated respiration varied in time andspace, and R<3µm did not dominate Rcomm. This is incontrast to the results of Smith & Kemp (2001) whofound that bacteria in <3 µm samples contributed>50% of the respiration in the mesohaline and poly-haline regions of Chesapeake Bay. Chl a concentra-tions in <3 µm samples were usually <1 µg l−1, whichis <10% of total chl a, at all sampling stations. Statis-tically significant differences in respiration rate werenot found in different size fractions at the freshwaterend-member, where approximately 80% of chl a con-sisted of >20 µm phytoplankton communities (graphsnot shown). The measurements carried out on ETM

surface water were highly variable (graphs notshown). However, consistent results were obtainedfrom measurements of ETM bottom water and theseawater end-member samples (Fig. 10). These re -sults indicate that approximately 85% of Rcomm wasperformed by organisms of size 3–10 µm in the earlyand late spring, when 61 and 74% of chl a was foundin the size range, respectively.

Linear regression analysis between BP<3µm andR<3µm resulted in r2 = 0.53 (p < 0.001) in early springand r2 = 0.74 (p < 0.001) in late spring, but it was notcorrelated in late winter (r2 = 0.06, p > 0.05). The esti-mated total BR indicated that the contribution of totalbacteria to average Rcomm was 61% in early springand 38% in late spring. In addition, BGE was esti-mated to be 0.18 in early spring and 0.19 in latespring.

74

Salinity R<3µm Temperature TSS Chl a Dinoflagellate Diatom Cryptophyte

Rcomm 0.34** 0.56** 0.58** −0.28** 0.46** 0.43** nr nrR<3µm nr 0.39** nr nr nr nr nrTemperature nr nr nr nr nr nrTSS nr nr nr nr nrChl a 0.53** 0.90** 0.30** 0.38**Dinoflagellate 0.42** nr 0.25**Diatom 0.44** nrCryptophyte 0.43** Cyanobacteria nr Chlorophyllide a 0.47** Pheophorbide a 0.69** Pheophytin a 0.48** Total pheophytin 0.34** BP nr BP<3µm nr

Cyano- Chloro- Pheo- Pheo- Total BP BP<3µm

bacteria phyllide a phorbide a phytin a pheophytin

Rcomm nr −0.23** nr 0.33** nr 0.44** 0.60**R<3µm 0.30** −0.27** nr 0.29** nr 0.27** 0.47**Temperature nr −0.22** nr 0.36** nr 0.37** 0.49**TSS 0.59** nr 0.63** 0.47** 0.70** nr −0.23**Chl a nr 0.39** nr nr −0.23** nr nrDinoflagellate nr 0.31** −0.24** nr −0.28** nr nrDiatom 0.32** 0.35** 0.30** 0.26** nr nr 0.32**Cryptophyte nr 0.25** nr nr nr nr nrCyanobacteria nr 0.66** 0.67** 0.59** nr 0.21**Chlorophyllide a nr −0.21** nr nr nrPheophorbide a 0.62** 0.73** nr nrPheophytin a 0.60** 0.37** 0.47**Total pheophytin nr nrBP 0.62**

Table 2. Partial correlation matrix of salinity and environmental and biological variables for the total water column with respi-ration. Rcomm: community respiration; R<3µm: respiration of plankton filtered through a 3 µm polycarbonate membrane filter;TSS: total suspended sediment; chl a: chlorophyll a; BP: total bacterial production; BP<3µm: production of bacteria filteredthrough a 3 µm polycarbonate membrane filter; nr: no relationship if r < 0.2; **p < 0.01; *0.01 < p < 0.05; n = 180, but n = 165

for the 2 bacterial production values

Lee et al.: Energy transfer in Chesapeake Bay 75

DISCUSSION

The purpose of the present study was to estimateplankton community metabolism and investigate thefundamental structure of the estuarine food web inthe Chesapeake Bay ETM region in relation to envi-ronmental and biological influences during the win-ter to spring period. We consistently observed higherand more variable metabolic rates seaward of theETM region between 45 and 80 km compared tobetween 0 and 45 km, which encompasses the fresh-water end-member and ETM region. These elevatedrates were associated with higher pigment concen-trations. Most of these metabolism results are compa-rable to previous studies in the upper ChesapeakeBay, although the mean GPP in late spring was about2-fold higher than that observed in previous work inthe upper bay (Smith & Kemp 1995, 2003). This discrepancy was likely caused by differences in thedefined seawater end-member stations betweenstudies and/or temporal variability in GPP.

The NEM results are dependent upon where wedraw the boundaries of our domain, because they arederived from knowledge of the spatial and temporalscales of hydrologic forcing and biological function-

ing. We used 2 domains (surface and water column)because the former provides insight into the shortand narrow scale of the food web in the surface layerbut the latter provides insight into the longer andbroader scale of the food web controlled by estuarinecirculation (Fig. 6). The NEM results suggest that insitu primary production supplied more organic mat-ter than was consumed in the surface layer in latewinter and late spring. However, higher river dis-charge in early spring increased the input of alloch -thonous organic matter relative to other seasons andresulted in net heterotrophy in the surface between40 and 60 km. Water column NEM resulted in strongnet heterotrophy due to respiration throughout thewater column. Net increases in dissolved organic car-bon have been reported in the ETM (Fisher et al.1998), which can be caused by zooplankton and bac-terial activity and also phytoplankton cell lysis asso-ciated with salinity changes. The heterotrophic activ-ity may also result in increasing organic aggregatesizes (Simon et al. 2002) and settling velocities ofETM particles (Sanford et al. 2001). Below the pycno -cline, organic matter may have originated from over-lying waters, lateral shallow areas, and advectivetransport from down-bay (Malone et al. 1986). Thus,net heterotrophy in the water column, especially bot-tom respiration, more likely resulted from diversesources of allochthonous organic matter.

Environmental factors controlling primary production

Near-surface assimilation numbers did not dropsignificantly in the ETM region on any of thecruises, suggesting that the phytoplankton therewere not compromised physiologically by low lightlevels. Also, increases in euphotic depth in the southwere not correlated with GPP on any of the individ-ual cruises, suggesting that light is not the most im -portant factor controlling variations in primary pro-duction in the ETM region. These results are incontrast to the findings of previous studies of theChesapeake Bay (Harding et al. 1986, Fisher et al.1999) and other estuaries (Cloern et al. 1983,Irigoien & Castel 1997), which have pointed to lightlimitation as the most likely factor driving variationsin GPP in the ETM region. This would, however,likely be true if GPP were compared to euphoticdepth throughout the entire bay due to significantlyshallower euphotic depths in the oligohaline com-pared to the meso- and polyhaline regions (seeFig. 2 in Smith & Kemp 1995).

Fig. 10. Size-fractionated respiration rates at (a) 66 km inearly spring and (b) 34 km, where the estuarine turbiditymaximum (ETM) was observed, in late spring in 2007.Tukey’s Studentized range test was used to investigate sta-tistical differences between groups. Different letters indi-cate significant differences (p < 0.05). Error bars equal esti-mates of the propagated standard error for a set of triplicate

samples

Mar Ecol Prog Ser 449: 65–82, 201276

Year-to-year variations in water properties and thedistribution of phytoplankton are tightly coupled tothe variation of the Susquehanna River discharge(Harding 1994), suggesting that there are direct influ-ences of physical mechanisms on the concentration ofphytoplankton in the bay. Peaks in river flow duringthe spring freshet generated strong salinity gradientsand, presumably, strong gravitational circulation,which increases the seaward and landward flow.Light limitation, mentioned above, is the result of theindirect effect of the river flow, but this does not ap-pear to be important in determining GPP variationswithin the ETM region. In contrast, the transport ofphytoplankton communities by surface and bottomwater flow appears to cause the inflow of freshwaterdiatoms from the Susquehanna River and predomi-nantly dinoflagellates from the southern part of theupper bay to the ETM region (Fig. 4). Thus, the latitu-dinal gradient of phytoplankton biomass, influencedby the hydrological process, appears to be the mostimportant factor determining the variation of GPP inthe ETM region. As in the upper Neuse River estuary,where hydrologic forcing controls phytoplankton dy-namics (Arhonditsis et al. 2007), the hydrological pro-cesses that effect the distribution of phytoplankton,and thus GPP, must be significant in this region, sincealong-channel current velocities can reach >60 cm s−1

near the ETM during a typical tidal cycle (Sanford etal. 2001) and a river flow of 3000 to 4000 m3 s−1 canproduce a mean seaward velocity of 12 to 15 cm s−1

above the ETM (Park et al. 2008). Furthermore, sea-ward of the ETM, where the water column isstratified and 2-layer circulation prevails, the tidallyaveraged along-channel residual current velocities ineach layer are around 10 cm s−1 and background ver-tical mixing is on the order of 10−6 m2 s−1 (Li et al.2005). Phytoplankton, like any suspended particle,are subjected to these hydrodynamic forces andmoved throughout the upper bay as a result.

One 3-dimensional modeling study that examinedthe transport and sedimentation of TSS (Park et al.2008) showed that the 15 d average seaward horizon-tal flux of TSS in the surface layer ranged between1.6 × 10−4 and 5.0 × 10−4 kg m−2 s−1 when river dis-charge was low-to-moderate and between 18.8 × 10−4

and 46.8 × 10−4 kg m−2 s−1 when river discharge washigh. Conversely, the landward horizontal flux ofTSS in the bottom layer ranged between 6.2 × 10−4

and 15.3 × 10−4 kg m−2 s−1 when river discharge waslow-to-moderate and between 12.3 × 10−4 and 21.8 ×10−4 kg m−2 s−1 when river discharge was high. If weassume that phytoplankton behave in a similar man-ner as do suspended sediments, although likely with

less sinking, then the model results by Park et al.(2008) suggest that substantial transport occurs. Thedynamics of this transport will cause freshwaterphytoplankton, and others that are in the surfacelayer to the south, to be transported seaward, whileany phytoplankton that are below the pycnocline, asthe dinoflagellates often were, will be transportednorth toward the ETM. During, or as a result of trans-port, hydrodynamic processes and bio-physical inter-actions may cause phytoplankton to become trappedin the ETM or to move across a salinity gradient witheither processes likely causing mortality or stress. Asa result freshwater phytoplankton and those frommore saline waters tend to form separate communi-ties, which will have different levels of GPP, on eitherside of the ETM and areas where the salinity gradi-ent is steep (i.e. these are salinity stress/mortalitybarriers to phytoplankton that prevent individualspecies from growing successfully throughout thewhole upper bay). Indeed, the stress caused whenphytoplankton are trapped in the ETM or cross asalinity gradient may be the primary reason why GPPwas low in certain areas, since stressed phytoplank-ton will not be able to grow as well as healthy ones.Thus, we suggest that the physical transport ofphytoplankton and the resulting bio-physical inter-actions are equally or more important than light lim-itation for explaining variations in GPP in the ETMregion.

Role of dinoflagellates

Correlation analyses indicate that autochthonousorganic matter originating from dinoflagellates wasan important source of carbon to the ecosystem in theoligohaline region during the winter to spring period,which may be attributable to the physiological attrib-utes of these organisms. Variations in dinoflagellatepigments alone explained much of the variability inGPP, with the second highest correlation, after chl a(Table 1). Regression analyses for each of the cruisesalso revealed that dinoflagellates were highly corre-lated with GPP, except in early spring (Fig. 8).

Dinoflagellates found in Chesapeake Bay arehighly adaptable to environmental changes (e.g.salinity, light availability, and nutrients) and manycan switch between heterotrophic and autotrophicfeeding modes (Stoecker 1998). This adaptabilitymay help to explain the high abundances of dinofla-gellates in the bay in winter and spring when envi-ronmental conditions rapidly change. These dinofla-gellates are often found below the compensation

Lee et al.: Energy transfer in Chesapeake Bay

depth, and are thought to use various mechanisms tosurvive under low light conditions during the lengthytransport from the mouth of the bay (Tyler & Seliger1978, Wofsy 1983, Cole et al. 1992). Harding (1988)found that dinoflagellates, specifically Prorocentrumminimum, are capable of enhancing the efficiency oflight harvest by increasing chl a and peridinin percell and by increasing the initial slope of the photo-synthesis−light curve. More importantly, the phago -trophic capability of mixotrophic dinoflagellates isparticularly beneficial because it can provide bothcarbon and nutrients in low light and low nutrientconditions. This feeding strategy has been found inmany dinoflagellate species in diverse environments(Sanders 1991, Stoecker et al. 1997).

Role of diatoms

The partial correlation results suggest that the con-tribution of diatoms to GPP was generally less thanthat of dinoflagellates. Diatom concentrations weregenerally lower than dinoflagellates, and they werenot correlated with GPP on any of the individualcruises, but they were significantly correlated withGPP over the entire data set (Table 1). However,when the salinity effect is not removed (i.e. normalcorrelation analysis), diatom concentrations signifi-cantly explained variations in GPP in early spring (r =0.52, p < 0.05) and late spring (r = 0.63, p < 0.01). Thissuggests that diatoms were more sensitive to salinitychanges than dinoflagellates and that mortality dueto salinity stress strongly affected the fate of diatomsin the oligohaline region.

It should also be noted that this sensitivity mightlead to underestimation of the contribution of dia -toms to bacterial and microheterotrophic production.Salinity changes are most rapid in the oligohaline,and they can influence the fate of phytoplanktonbecause the optimal salinity ranges of phytoplanktonare species specific. Although the estuarine phyto-plankton community has a wider salinity tolerancecompared to oceanic and coastal species (Brand1984), it is clear that different phytoplankton commu-nities were responsible for autotrophic production inthe different salinity regimes that we sampled, i.e.freshwater diatoms dominated the flora in the fresh-water to oligohaline transition and marine/estuarinedinoflagellates dominated the flora in the oligohalineto mesohaline transition (Fig. 4). The transition be -tween these 2 dominant floral groups was abrupt,and it generally happened across the 0 to 1 isohalinesin the ETM region. These results suggest that diatom

mortality rates were high in this region, likely due toosmotic stress. Similarly, in the Schelde Estuary,freshwater phytoplankton communities showed theweakest ability to adapt to seawater and rapidly dis-appeared and were replaced by estuarine specieswhen salinity increased to >0.5 (Muylaert et al.2000). Thus, physiological (cellular lysis) and physi-cal (sinking loss) processes may prevent diatomsfrom growing in the ETM. However, the death ofthese cells in the ETM may release dissolved organicmatter (Fisher et al. 1998), which can be consumedby bacteria and microheterotrophs, thus contributingto secondary production.

In addition, the importance of diatoms might beunderestimated by the exclusion of benthic diatomsin the analysis. Benthic diatoms cannot inhabit thedeep shipping channel in Chesapeake Bay due tolight limitation. This impediment is worsened in theoligohaline due to high concentrations of TSS, whichcause rapid light attenuation. However, filamentousalgae and chain-forming diatoms observed beneathice cover on the late-winter cruise in 2007 suggestthat, in years with ice, diatoms may contributeorganic matter to secondary producers. Ichinomiya etal. (2009) estimated that approximately 50% ofdiatoms released from ice are exploited by bothpelagic and benthic grazers, while only 3% of diatomloss is attributable to sinking. Thus, if there was rapidand selective grazing on diatoms by abundant cope-pods and microzooplankton, a significant fraction ofice-attached diatoms might be consumed by thepelagic grazers. Although surface ice formation inthe oligohaline Chesapeake Bay is not an annualevent, further study is needed to estimate the contri-bution of ice-attached diatoms.

Comparison between primary and bacterial production

GPP in general contributed more organic carbonthan BP, although this varied in space and time(Fig. 7). In addition, BP in the ETM was relatively lowcompared to the region between 45 and 80 km. Thisis unexpected because TSS should provide sub-strates for particle-attached bacteria, which are moreproductive than free-living bacteria on a per-cellbasis (Crump et al. 1998). One possible explanation isthat TSS in Chesapeake Bay is less labile in the win-ter to spring period than in summer when detritalorganic matter comes from growing plants. This is incontrast to the findings of Findlay et al. (1991) whoobserved higher BP than phytoplankton production

77

Mar Ecol Prog Ser 449: 65–82, 201278

in the tidally influenced area of the Hudson Riverestuary from spring to fall. In the Hudson River, highTSS concentrations lead to rapid light attenuation,with diffuse attenuation coefficients reaching valuesof 10 m−1 during spring runoff (Cole et al. 1992). Incontrast, the maximum coefficient measured in ourstudy was 4.4 m−1 in early spring. In addition, dinofla-gellates, which require strong stratification of thewater column in order to use it as a migration path-way (Tyler & Seliger 1978), would likely not be ableto reach the oligohaline of the Hudson River estuarybecause the water column in the oligohaline is wellmixed (Fisher et al. 1988). We therefore speculatethat the smaller diffuse attenuation coefficient,strong stratification, and, possibly, more refractoryTSS in the winter to spring period in Chesapeake Bayare key factors that promote higher GPP than BP inthe oligohaline.

As with phytoplankton, bacteria originating fromfreshwater would likely suffer from osmotic stress.Painchaud et al. (1987) described massive losses ofriverine bacteria as they flow into the salinity conver-gence zone in the St. Lawrence Estuary. This proba-ble zone of high bacteria and phytoplankton mortal-ity could provide an important source of organicmatter for the ETM region. This idea is supported bythe relationship between dissolved organic matterand salinity in the upper Chesapeake Bay, which isbest fit with a convex second-order polynomial func-tion (Fisher et al. 1998), indicating net dissolvedorganic matter production in the ETM region. This isin contrast to the idea that the production of dis-solved organic matter in the ETM region is derivedfrom bacterial colonization and dissolution of detritusfrom allochthonous sources. However, the insignifi-cant relationship between BP and chlorophyllide a(Table 2), an indicator of senescent diatoms, suggeststhat microbial communities did not appear to con-sume dissolved organic matter released from senesc-ing diatoms, possibly because bacteria are alsostressed by salinity changes along with phytoplank-ton. All of these lines of evidence suggest that theETM region is an area of weak microbial hetero -trophic activities and high salinity stress that causesthe death of phytoplankton and bacteria, which in -creases dissolved organic matter concentrations.

Phytoplankton pigment analyses

Strong correlations were found between Rcomm andchl a and dinoflagellates; these strongly suggest that‘autotrophic organisms’ were responsible for not only

GPP, but also a large fraction of Rcomm in the oligoha-line region (Fig. 9, Table 2). These correlations couldbe caused by very tight coupling between auto -trophic production and heterotrophic consumption,e.g. bacterial consumption of labile organic matterfreshly released by phytoplankton. However, exceptin early spring, the high chl a concentrations down-estuary of ETM were largely due to the presence ofmixotrophic dinoflagellates. It therefore seems morelikely that the combined autotrophic and heterotro-phic capability of these dinoflagellates explains whythere were such strong correlations between dinofla-gellates, GPP and Rcomm. Size-fractionated experi-ments also support this conclusion. A large fraction ofRcomm was due to organisms in the 3–10 µm sizerange in the south and ETM bottom water (Fig. 10)where approximately 68% of total chl a was found,which is consistent with a major contribution ofmixotrophic dinoflagellates. The dinoflagellate Hete-rocapsa rotundatum, which was common in our sam-ples, has an equivalent spherical diameter of 5.8 µm(Jeong et al. 2005), and thus likely passed throughthe 10 µm screen, but not through the 3 µm screen.

Note that particle-attached bacteria can also con-tribute to respiration in the 3–10 µm fraction. To findthe relative contribution of bacteria and dinoflagel-lates, we assumed that the bulk of respiration in the<10 µm size samples was from mixotrophic dinofla-gellates and bacteria, and in the <3 µm size sampleswas only from bacteria. Then, we estimated that totalbacteria contributed approximately 61 and 38% oftotal Rcomm in early spring and late spring, respec-tively. We also assumed that 85% of Rcomm, which isthe mean percentage of respiration rates of the<10 µm size samples (Fig. 10), is due to bacteria anddinoflagellates. Therefore, the contribution of mixo -trophic dinoflagellates to Rcomm was approximately24% in early spring and 47% in late spring. In earlyspring, dinoflagellate concentrations were the lowestand regression analysis also suggested that the con-tribution of dinoflagellates in explaining communitymetabolism was insignificant in early spring. How-ever, the respiration of mixotrophic dinoflagellateswas higher than that of the bacteria in late spring.

Our calculation of dinoflagellate contribution toRcomm would be biased if the factor converting BP toBR were different for <3 and >3 µm bacteria. Theconversion factor for the latter is presumably higherdue to organic substrates on particles (Crump et al.1998). To check the validity of this calculation, weestimated BGE of the bacterial community, whichresulted in 0.18 in early spring and 0.19 in latespring. These BGE estimates fit well in the distribu-

Lee et al.: Energy transfer in Chesapeake Bay

tion of BGE in the Chesapeake Bay (Appleet al. 2006), and they are consistent withthe observation that BR increases withincreasing temperature (Shiah & Ducklow1994), which apparently resulted in lowBGE in spring. In addition, as discussedabove, TSS might not be as labile, thussuppressing the metabolic activities of par-ticle-attached bacteria in the ETM region,which is consistent with the fact that therewas no significant correlation betweentotal BP and TSS (Table 2). This evidencesuggests that our assumption that differentsizes of bacteria share the same respirationefficiency is reasonable. However, BP andBR are still expected to vary depending onriver discharge, temperature, and the quality oforganic matter. Therefore, we need further informa-tion on bacterial abundance in the 2 groups, the sizeof particle-attached bacteria, and the rate of bacterialmetabolism with and without organic substrates tobetter define the contributions of bacteria anddinoflagellates to Rcomm.

The high correlations between Rcomm and degradedphytoplankton pigments (except chlorophyllide a)also suggest that the rapid consumption of phyto-plankton-derived organic matter resulted in in -creased Rcomm (Table 2). The distribution and concen-tration of pigment degradation products in the ETMregion is very likely dictated by physiological stress,physical entrapment, and heterotrophic consumption(Lemaire et al. 2002). The different spatial distribu-tion of chlorophyllide a, indicating senescent diatomsin contrast to other pheopigments, is consistent withthe idea that diatoms experienced physiologicalstress (discussed above). That is, rapid salinitychanges may play an important role in formingchlorophyllide a, perhaps due to the low tolerance ofdiatoms to salinity changes.

Dinoflagellates and implications for the food web

The ETM region of Chesapeake Bay is a mixingzone for a continuous supply of river-borne diatoms,inorganic and organic nutrients, bacteria, TSS, andhighly productive estuarine dinoflagellates. Together,these materials provide carbon sources for omnivo-rous mesozooplankton (Fig. 11). We speculate thatchanges in salinity and other physical mechanismsbreak down and trap particulate and dissolved or-ganic matter mainly from the Susquehanna River inthe ETM region. Measured GPP, Rcomm, and BP are

low in the ETM, suggesting that much of this organicmatter passes through the ETM region to the southwhere it supports the growth of bacteria. The micro-bial community would be further supported by the in-put of inorganic and organic matter from the mesoha-line region of the bay via landward-flowing bottomwater.

We hypothesize that the bacteria, along with cryp-tophytes, are consumed by mixotrophic dinoflagel-lates, and that these dinoflagellates therefore play animportant role in the food web by consuming organicmatter from the detrital food web and also by fixingcarbon and nutrients autotrophically. Dinoflagellatesmoving between the down-estuary region and theETM could switch between heterotrophic andautotrophic modes of nutrition to maximize growth(Stoecker 1998). However, it should be noted that thetime it takes to switch can vary depending on themixotrophic species and also light, nutrient, and foodavailability (Sanders et al. 1990). It is also unclearwhether or not the physiological change is unidirec-tional from heterotroph to autotroph. It is thereforeprobable that individual dinoflagellates possess different degrees of mixotrophic balance, from pri-marily heterotrophic to intermediate to primarilyauto trophic. Regardless, the positive relationshipsbe tween dinoflagellate pigments and both GPP andRcomm during winter to spring suggest that dinoflagel-lates between 45 and 80 km performed photosynthe-sis and obtained energy by feeding heterotrophicallyon bacteria and cryptophytes.

The organic matter supply from diverse phyto-plankton groups, bacteria, and external loading cangive rise to abundant and diverse secondary pro -ducers. Mesozooplankton can fulfill their carbonrequirements in ETM regions by selectively grazingon phytoplankton, by filtering out detrital organic

79

Fig. 11. Energy flow diagram illustrating the estuarine food web in theChesapeake Bay estuarine turbidity maximum (ETM) region. FW: freshwater; SW: sea water; POM: particulate organic matter; DOM:

dissolved organic matter; dotted line: pycnocline

Mar Ecol Prog Ser 449: 65–82, 2012

matter, or by grazing on microzooplankton (Van denMeersche et al. 2009). The low GPP in the Chesa-peake Bay ETM suggests that copepod diets wouldbe composed of a variety of food items. However,dinoflagellates are estimated to have about twice thecaloric content (the sum of protein, carbohydrate,and lipid) of diatoms of similar volume (Hitchcock1982). Moreover, copepod egg production has beenshown to be highly correlated with the ingestion ofdinoflagellates (Kleppel et al. 1991). Also, due to thehigh grazing ability and preference for dinoflagel-lates by copepods, most dinoflagellate biomass isconsumed in the water column and sinking loss to thebottom is negligible (Sellner et al. 1991, 1992). Thesefacts, combined with the results from the presentstudy, lead us to conclude that the Chesapeake BayETM region is an area of relatively low microbialactivity and high dinoflagellate productivity thatdirectly supports mesozooplankton and higher tro -phic levels rather than being an area supporting ahigh efficiency ‘microbial shunt.’

Acknowledgements. We thank the captain and crew of theRV ‘Hugh R. Sharp’ for their assistance on the researchcruises to the Chesapeake Bay ETM. We also thank E.Houde, M. Roman, L. Sanford, E. North, J. Pierson, S-Y.Chao, D. Kimmel, Y. Kim, S. Suttles, E. Kiss, and all of themembers of the BITMAXII project for their assistance in col-lecting water samples and analyzing data. We are especiallygrateful to L. Codispoti and V. Kelly for providing D.Y.L.with training in high precision oxygen measurement meth-ods. This work was supported by the National Science Foun-dation (Grant OCE-0453905). This is UMCES contributionno. 4631.

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Editorial responsibility: Antonio Bode,A Coruña, Spain

Submitted: February 23, 2011; Accepted: December 5, 2011Proofs received from author(s): February 29, 2012